Search Results (187)

Silicon photonics has emerged as a critical enabling technology for a diverse range of applications, from high-speed data communication and computing to advanced sensing and quantum information processing. This paper provides a comprehensive review of recent progress in the foundational passive devices that underpin this technological revolution. We survey the state of the art in fundamental building blocks, including strip, rib, and silicon nitride waveguides, with a focus on achieving ultra-low propagation loss. The review details essential components for light coupling and splitting, such as grating couplers, edge couplers, multimode interference couplers, and directional couplers, citing their typical performance metrics. Key wavelength filtering and routing components, including high-Q ring resonators, Mach–Zehnder interferometers, and arrayed waveguide gratings, are analyzed. Furthermore, we provide a comparative overview of the capabilities of major photonic foundries operating on a multi-project wafer model. The paper concludes by discussing persistent challenges in packaging and polarization management, and explores future trends driven by co-packaged optics, inverse design methodologies, and the expansion of silicon photonics into new application domains. Full article

This paper aims to address the possibility of parametric resonance effects in microtubules via tryptophan qubits, using the Hamiltonian of the cavity quantum electrodynamics (QED) model involving photons in a waveguide and the surrounding environment. The time evolution equations for qubits and photons are derived using the input–output formulation. Input signals with a 560 nm wavelength are amplified by Rabi oscillations for tryptophan qubits in excited states. Here, the qubits organized in multiple layers are all in excited states. When an appropriate decay to the environment occurs as internal loss, which is prepared in multiple layers, we find binary patterns of the parametric amplification of input signals and the reduction of output signals. This property might help us to understand the information processing of optical signals by filtering them with the use of tryptophan residues in microtubules and diffused nonlocal processing spreading over the whole brain in the form of holograms. Full article

This innovative study introduces an eco-conscious and cost-effective approach to synthesizing gelatin-based carbon dots (CDs) via two distinctive methods: hydrothermal processing in a muffle furnace (CDs-MF) and domestic microwave (CDs-MW). Both strategies harness natural, low-cost materials and prioritize simplicity, sustainability, and environmental friendliness, culminating in effective fluorescent sensing of the pesticide thiamethoxam (TMX). For the hydrothermal route, the investigation explores two purification approaches—ultracentrifugation (CDs-MF-C) and 0.22 µm syringe filtration (CDs-MF-F)—while the microwave-derived CDs (CDs-MW) undergo dialysis alone. This study aims to investigate how synthesis and purification impact the CDs structural, morphological, and photophysical characteristics. The difference in size was obtained from transmission electron microscopy (TEM): 30–40 nm for CDs-MF-C, 12–15 nm for CDs-MF-F, and 3–6 nm for CDs-MW. Fluorescence emission performance reveals that CDs-MF-F performs a fluorescence quantum yield of 27%, CDs-MF-C at 23%, and CDs-MW at a modest 3%. All variants exhibit TMX detection via fluorescence quenching through the inner filter effect (IFE). Analytically, CDs-MF-C stands out with the lowest detection limit (LOD = 0.396 ppm) and quantification limit (LOQ = 1.317 ppm), followed by CDs-MF-F (LOD = 0.475 ppm; LOQ = 1.585 ppm) and CDs-MW (LOD = 0.549 ppm; LOQ = 1.831 ppm). These findings emphasize the unique interplay between the synthesis pathway, purification strategy, and functional performance, demonstrating the critical importance of tuning structural properties for optimizing carbon-dot sensors. Full article

Short-wave-infrared (SWIR) imaging can extend drone operations into fog and rain, yet the optimum spectral strategy remains unclear. We evaluated a drone-borne quantum-dot SWIR camera inside a climate-controlled tunnel that generated calibrated advection fog, radiation fog, and rain. Images were captured with a broadband 400–1700 nm setting and three sub-band filters, each at four lens apertures (f/1.8–5.6). Entropy, structural-similarity index (SSIM), and peak signal-to-noise ratio (PSNR) were computed for every weather–aperture–filter combination. Broadband SWIR consistently outperformed all filtered configurations. The gain stems from higher photon throughput, which outweighs the modest scattering reduction offered by narrowband selection. Under passive illumination, broadband SWIR therefore represents the most robust single-camera choice for unmanned aerial vehicles (UAVs), enhancing situational awareness and flight safety in fog and rain. Full article

Quantum sensing based on NV-centers in diamonds has been demonstrated many times in multiple publications. The majority of publications use lasers in free space or lasers with fiber optics, expensive optical components such as dichroic mirrors, or beam splitters with dichroic filters and expensive detectors, such as Avalanche photodiodes or single photon detectors, overall, leading to custom and expensive setups. In order to provide an inexpensive NV-based magnetometer setup for educational use in schools, to teach the three topics, fluorescence, optically detected magnetic resonance, and Zeeman splitting, inexpensive, miniaturized, off-the-shelf components with high reliability have to be used. The cheaper such a setup, the more setups a school can afford. Hence, in this work, we investigated LEDs as light sources, considered different diamonds for our setup, tested different color filters, proposed an inexpensive microwave resonator, and used a cheap photodiode with an appropriate transimpedance amplifier as the basis for our quantum magnetometer. As a result, we identified cheap and functional components and present a setup and show that it can demonstrate the three topics mentioned at a hardware cost <EUR 100. Full article

Graphene quantum dots (GQDs) obtained by microwave-induced pyrolysis of glutamic acid and triethylenetetramine (trien) are fairly stable, emissive, water-soluble, and positively charged nano-systems able to interact with negatively charged meso-tetrakis(4-sulfonatophenyl) porphyrin (TPPS₄). The stoichiometric control during the preparation affords a supramolecular adduct, GQDs@TPPS₄, that exhibits a double fluorescence emission from both the GQDs and the TPPS₄ fluorophores. These supramolecular aggregates have an overall negative charge that is responsible for the condensation of cations in the nearby aqueous layer, and a three-fold acceleration of the metalation rates of Cu²⁺ ions has been observed with respect to the parent porphyrin. Addition of various metal ions leads to some changes in the UV/Vis spectra and has a different impact on the fluorescence emission of GQDs and TPPS₄. The quenching efficiency of the TPPS₄ emission follows the order Cu²⁺ > Hg²⁺ > Cd²⁺ > Pb²⁺ ~ Zn²⁺ ~ Co²⁺ ~ Ni²⁺ > Mn²⁺ ~ Cr³⁺ >> Mg²⁺ ~ Ca²⁺ ~ Ba²⁺, and it has been related to literature data and to the sitting-atop mechanism that large transition metal ions (e.g., Hg²⁺ and Cd²⁺) exhibit in their interaction with the macrocyclic nitrogen atoms of the porphyrin, inducing distortion and accelerating the insertion of smaller metal ions, such as Zn²⁺. For the most relevant metal ions, emission quenching of the porphyrin evidences a linear behavior in the micromolar range, with the emission of the GQDs being moderately affected through a filter effect. Deliberate pollution of the samples with Zn²⁺ reveals the ability of the GQDs@TPPS₄ adduct to detect sensitively Cu²⁺, Hg^2+, and Cd²⁺ ions. Full article

Optimization has always been viewed as a central component of many electrical engineering techniques, where it involves designing a complex system with various constraints and competing objectives. The method described in this work proposes a hybrid quantum–classical evolutionary optimization algorithm targeting high-frequency electromagnetic problems. A genetic algorithm with a quantum selection operator that applies high selection pressure while preserving selection diversity is introduced. This change means that stagnation can be reduced without compromising the speed of convergence. This was used on both real quantum hardware as well as quantum simulators. The results demonstrate that the performance of the real quantum devices was deteriorated by the noise in these devices and that simulators would be a useful option. We provide a description of the operation of the proposed evolutionary optimization method with mathematical benchmarks and electromagnetic design problems that show that it outperforms conventional evolutionary algorithms in terms of convergence behavior and robustness. Full article

Quantum memory is essential for the prolonged storage and retrieval of quantum information. Nevertheless, no current studies have focused on the creation of effective quantum memory for continuous variables while accounting for the decoherence rate. This work presents an effective continuous-variable quantum key distribution method with parameter optimization utilizing the Elitist Elk Herd Random Immigrants Optimizer (2E-HRIO) technique. At the outset of transmission, the quantum device undergoes initialization and authentication via Compressed Hash-based Message Authentication Code with Encoded Post-Quantum Hash (CHMAC-EPQH). The settings are subsequently optimized from the authenticated device via 2E-HRIO, which mitigates the effects of decoherence by adaptively tuning system parameters. Subsequently, quantum bits are produced from the verified device, and pilot insertion is executed within the quantum bits. The pilot-inserted signal is thereafter subjected to pulse shaping using a Gaussian filter. The pulse-shaped signal undergoes modulation. Authenticated post-modulation, the prediction of link failure is conducted through an authenticated channel using Radial Density-Based Spatial Clustering of Applications with Noise. Subsequently, transmission occurs via a non-failure connection. The receiver performs channel equalization on the received signal with Recursive Regularized Least Mean Squares. Subsequently, a dataset for side-channel attack authentication is gathered and preprocessed, followed by feature extraction and classification using Adaptive Depthwise Separable Convolutional Neural Networks (ADS-CNNs), which enhances security against side-channel attacks. The quantum state is evaluated based on the signal received, and raw data are collected. Thereafter, a connection is established between the transmitter and receiver. Both the transmitter and receiver perform the scanning process. Thereafter, the calculation and correction of the error rate are performed based on the sifting results. Ultimately, privacy amplification and key authentication are performed using the repaired key via B-CHMAC-EPQH. The proposed system demonstrated improved resistance to decoherence and side-channel attacks, while achieving a reconciliation efficiency above 90% and increased key generation rate. Full article

Cancer remains one of the most formidable challenges to human health; hence, developing effective treatments is critical for saving lives. An important strategy involves reactivating tumor suppressor genes, particularly p53, by targeting their negative regulator MDM2, which is essential in promoting cell cycle arrest and apoptosis. Leveraging a drug repurposing approach, we screened over 24,000 clinically tested molecules to identify new MDM2 inhibitors. A key innovation of this work is the development and application of a selective cleaning algorithm that systematically filters assay data to mitigate noise and inconsistencies inherent in large-scale bioactivity datasets. This approach significantly improved the predictive accuracy of our machine learning model for pIC₅₀ values, reducing RMSE by 21.6% and achieving state-of-the-art performance (R² = 0.87)—a substantial improvement over standard data preprocessing pipelines. The optimized model was integrated with structure-based virtual screening via molecular docking to prioritize repurposing candidate compounds. We identified two clinical CB1 antagonists, MePPEP and otenabant, and the statin drug atorvastatin as promising repurposing candidates based on their high predicted potency and binding affinity toward MDM2. Interactions with the related proteins MDM4 and BCL2 suggest these compounds may enhance p53 restoration through multi-target mechanisms. Quantum mechanical (ONIOM) optimizations and molecular dynamics simulations confirmed the stability and favorable interaction profiles of the selected protein–ligand complexes, resembling that of navtemadlin, a known MDM2 inhibitor. This multiscale, accuracy-boosted workflow introduces a novel data-curation strategy that substantially enhances AI model performance and enables efficient drug repurposing against challenging cancer targets. Full article

Accurate wind-speed prediction plays an important role in improving the operation stability of wind-power generation systems. However, the inherent complexity of meteorological dynamics poses a major challenge to forecasting accuracy. In order to overcome these limitations, we propose a new hybrid framework, which combines variational mode decomposition (VMD) for signal processing, enhanced quantum particle swarm optimization (e-QPSO), an improved walking optimization algorithm (IHOA) for feature selection and the long short-term memory (LSTM) network, and which finally establishes a reliable prediction architecture. The purpose of this paper is to optimize VMD by using the e-QPSO algorithm to improve the problems of excessive filtering or error filtering caused by parameter problems in VMD, as the noise signal cannot be filtered completely, and the number of sources cannot be accurately estimated. The IHOA algorithm is used to find the optimal hyperparameters of LSTM to improve the learning efficiency of neurons and improve the fitting ability of the model. The proposed e-QPSO-VMD-IHOA-LSTM model is compared with six established benchmark models to verify its predictive ability. The effectiveness of the model is verified by experiments using the hourly wind-speed data measured in four seasons in Changchun in 2023. The MAPE values of the four datasets were 0.0460, 0.0212, 0.0263, and 0.0371, respectively. The results show that e-QPSO-VMD effectively processes the data and avoids the problem of error filtering, while IHOA effectively optimizes the LSTM parameters and improves prediction performance. Full article

Fluorescence lateral flow assays (FLFA) based on quantum dot probes have attracted significant attention in recent years due to their high sensitivity and quantitative detection capabilities. FLFA requires the use of a straightforward fluorescence reader for quantitative detection. Most fluorescence readers employ narrowband filters for auxiliary imaging, which facilitates the acquisition of high-contrast signals. However, during trace detection, the weak signal from FLFA can be easily lost due to optical flux loss associated with narrowband filters, thereby indirectly diminishing detection sensitivity. To address this issue, we developed a fluorescence signal reader that employs CMOS imaging without optical filters and proposed a highly sensitive signal detection algorithm based on continuous wavelet transform (CWT) to identify weak fluorescence signals with low contrast. Experimental results demonstrate that the method achieves a fluorescence detection sensitivity for quantum dots of 10⁻¹⁰ mol/L, with a relative standard deviation (RSD) of < 1.45%. The designed filter-free detection system and CWT analysis algorithm were applied to various FLFA systems (including the sandwich method and the competition method), with the correlation coefficient (R²) between all detection results and sample concentration exceeding 0.997. The findings of this study offer a highly sensitive signal detection method for the precise quantification of FLFA. Full article

Accurately estimating a sequence of latent variables in state observation models remains a challenging problem, particularly when maintaining coherence among consecutive estimates. While forward filtering and smoothing methods provide coherent marginal distributions, they often fail to maintain coherence in marginal MAP estimates. Existing methods efficiently handle discrete-state or Gaussian models. However, general models remain challenging. Recently, a recursive Bayesian decoder has been discussed, which effectively infers coherent state estimates in a wide range of models, including Gaussian and Gaussian mixture models. In this work, we analyze the theoretical properties and implications of this method, drawing connections to classical inference frameworks. The versatile applicability of mixture models and the prevailing advantage of the recursive Bayesian decoding method are demonstrated using the double-slit experiment. Rather than inferring the state of a quantum particle itself, we utilize interference patterns from the slit experiments to decode the movement of a non-stationary particle detector. Our findings indicate that, by appropriate modeling and inference, the fundamental uncertainty associated with quantum objects can be leveraged to decrease the induced uncertainty of states associated with classical objects. We thoroughly discuss the interpretability of the simulation results from multiple perspectives. Full article

Incessant urbanization and industrialization have resulted in several pollutants being increasingly produced and continuously discharged into the environment, altering its equilibrium, with a high risk for living organisms’ health. To restore it, new advanced materials for remediating gas streams, polluted soil, water, wastewater, groundwater and industrial waste are continually explored. Carbon-based nanomaterials (CNMs), including quantum dots, nanotubes, fullerenes and graphene, have displayed outstanding effectiveness in the decontamination of the environment by several processes. Carbon nanotubes (CNTs), due to their nonpareil characteristics and architecture, when included in absorbents, filter membranes, gas sensors, etc., have significantly improved the efficiency of these technologies in detecting and/or removing inorganic, organic and gaseous xenobiotics and pathogens from air, soil and aqueous matrices. Moreover, CNT-based membranes have displayed significant potential for efficient, fast and low-energy water desalination. However, despite CNTs serving as very potent instruments for environmental detoxification, their extensive utilization could, paradoxically, be highly noxious to the environment and, therefore, humans, due to their toxicity. The functionalization of CNTs (F-CNTs), in addition to further enhancing their absorption capacity and selectivity, has increased their hydrophilicity, thus minimizing their toxicity and carcinogenic effects. In this scenario, this review aims to provide evidence of both the enormous potential of CNTs in sustainable environmental remediation and the concerning hazards to the environment and living organisms that could derive from their extensive and uncontrolled utilization. To this end, an introduction to CNTs, including their eco-friendly production from biomass, is first reported. Several literature reports on CNTs’ possible utilization for environmental remediation, their potential toxicity due to environmental accumulation and the challenges of their regeneration are provided using several reader-friendly tools, to better capture readers’ attention and make reading easier. Full article

High-temperature thermal images enable monitoring and controlling processes in metal, semiconductors, and ceramic manufacturing but also monitor activities of volcanoes or contrasting wildfires. Infrared thermal cameras require knowledge of the emissivity coefficient, while multispectral pyrometers provide fast and accurate temperature measurements with limited spatial resolution. Bayer-pattern cameras offer a compromise by capturing multiple spectral bands with high spatial resolution. However, temperature estimation from color remains challenging due to spectral overlaps among the color filters in the Bayer pattern, and a widely accepted calibration method is still missing. In this paper, the quantum efficiency of an imaging system including the camera sensor, lens, and filters is inferred from a sequence of images acquired by looking at a black body source between 700 °C and 1100 °C. The physical model of the camera, based on the Planck law and the optimized quantum efficiency, allows the calculation of the Planckian locus in the color space of the camera. A regression neural network, trained on a synthetic dataset representing the Planckian locus, predicts temperature pixel by pixel in the 700 °C to 3500 °C range from live images. Experiments done with a color camera, a multispectral camera, and a furnace for heat treatment of metals as ground truth show that our calibration procedure leads to temperature prediction with accuracy and precision of a few tens of Celsius degrees in the calibration temperature range. Tests on a temperature-calibrated halogen bulb prove good generalization capability to a wider temperature range while being robust to noise. Full article

In this paper, we present a simple solvothermal method to synthesize highly fluorescent metal-free elemental selenium quantum dots (SeQDs) using cost-effective bulk selenium powder. The SeQDs exhibit a small and uniform size, excellent aqueous dispersibility, a high photoluminescence quantum yield (PLQY) of 19.3% with stable fluorescence, and scalable production with a 7.2% yield. Owing to the inner filter effect (IFE), these SeQDs function as a highly effective nanoprobe for Cr(VI) detection, exhibiting exceptional sensitivity (detection limit: 145 nM) and selectivity over a wide linear range (5–105 μM), along with rapid response kinetics. Moreover, SeQDs show low cytotoxicity and efficient cellular uptake, enabling cell imaging and intracellular Cr(VI) monitoring. Significant fluorescence quenching in Cr(VI)-exposed cells confirms the potential of SeQDs as a viable fluorescent nanoprobe for Cr(VI) detection in complex cellular environments. This work thus not only establishes a simple method for the preparation of fluorescent SeQDs but also develops a promising fluorescent nanoprobe for cell imaging and Cr(VI) sensing. Full article